If the past decade was a period of rapid digitalization, the current decade will be about consolidating this transformation by advancing innovation at an even faster pace.
Software development has undergone significant revolutions with varying themes through the years. There was a time of a massive shift to agility that promoted iterative and collaborative development. Another era was led by microservices and API, enabling developers to build smaller apps and build with fewer resources.
In all of these, the culture has flowed top-down. Therefore, as we step into a new era of software development focusing on building smarter and more innovative software, it is essential to know what industry leaders are doing to lead the frontline of innovation as their responsibility entails.
Therefore, this article explores studies involving tech leaders across various spheres to identify the trends of the most important thoughts and ideas expected to impact how software is developed in 2021 and the coming years.
No-Code is the software development approach that requires little or no programming skill. This makes it possible for individuals with no knowledge of programming skills to edit apps through drag-and-drop and similar visual processes.
The growing popularity of no-code platforms will change the way we think about software and open up a new frontier for a whole new group of “developers.”
For Wade Foster, CEO of Zapier, no-code development is about empowerment. In terms of the future of no-code development, he is highly optimistic and believes that:
- There would be more no-code than coding products in less than a year.
- The first no-code product would go public within the next five years.
- More agencies and dev shops would use no-code tools than otherwise within the next five years.
He’s not the only one who shares these high hopes. Gartner predicted that 65% of all app development would be powered by low code by 2024, which is being hailed as a revolution.
Indeed, no-code development democratizes the development process by bringing the power to build scalable technology solutions to a much wider audience and making it easy to use across all industries. No-code solutions are sure to accelerate innovation and the introduction of new software products. After all, anyone with a good idea can start a new business with a highly scalable, efficient, and customizable product.
Testing and DevOps
One of the main trends in software testing in recent times is the incorporation of Agile and DevOps.
When asked what testing best practices he recommends to avoid release delays, Brendan O’Leary, a senior development evangelist at GitLab, said, “The more you can consolidate, automate, and integrate testing into your entire DevOps flow, the larger dividends will be paid down the road.”
When testers use disparate tools to create and run tests, and developers use different tools to deploy the product, the proliferation of various tools makes it difficult for organizations to establish a unified DevOps flow where teams collaborate and share information more easily. As a result, these organizations often find themselves with testers and developers duplicating efforts and working at cross-purposes.
O’Leary also recommends starting with automated testing rather than including it at the latter stages. In his words, “While it can be an upfront investment to get started, the key will be that once established, an automated testing program will be much easier to iterate on than starting from scratch.”
Once you have a solid foundation in place, such as a deployment pipeline with automated testing, it is much easier to integrate additional testing tools for specific use cases. This is where DevOps practices can help accelerate your overall development cycle and scale software delivery performance.
Data-driven software development is an emerging software engineering methodology that promotes data for engineering insights, code refactoring, and automated decision-making.
According to Satya Nadella, Microsoft’s CEO and new board chair, “In the past, when you thought about software (development), you thought about people writing the code. In software 2.0, you train the software to learn from data.” Nadella was talking about the shift toward a data-driven approach to software engineering.
Data-driven software engineering is not a new concept, but it has found new applications via advancements in artificial intelligence and the internet of things. Python, for example, has soared in popularity because of its integration with data science and artificial intelligence.
Because data-driven engineering coheres with business operations better than traditional approaches to software engineering, this method is sure to gain even more prominence as businesses strive to build scalable systems.
Data-driven software is central to modern software engineering. Companies like Google, Amazon, and Facebook have built their success on data-driven culture. They run tens of thousands of experiments every day, using data to make better decisions about everything, including product design, hiring algorithms, and customer service.
Data science is transforming how businesses operate by providing more accurate, actionable information about customers and products. It has also radically changed how software engineers work by enabling them to build products that are more responsive to customer experience.
We are still years away from the proliferation of AI technologies writing complex code on their own. In fact, artificial intelligence remains a high-value skill for programmers today. However, we see the emergence of AI programming assistants that help developers write code and program more efficiently.
According to Deepak Gupta, co-founder and CEO at LoginRadius, “Programmers devote a significant amount of effort to reviewing documentation and troubleshooting code. Developers can save time using intelligent programming assistants to provide in-the-moment guidance and recommendations such as best practices, code examples and relevant documents.”
Obviously, this goes beyond intelligent code completion, as it includes full-fledged assistants for programmers. Intelligent programming assistants are built on natural language processing (NLP), deep learning, and artificial intelligence. They are designed to augment the capabilities of developers by providing contextual guidance and recommendations.
The assistants can instantly understand what a developer is working on, provide guidance on how to resolve problems, help find code samples and documentation, and identify areas where code can be optimized.
A New Revolution
Software is changing the world, which is why the trends in development are so important to watch.
Every major industry is being revolutionized by the digital transformation taking place, which is being led by software innovation. The speed and complexity of software innovation are accelerating, and we need to be prepared. The ability to be agile and adapt to change is crucial for organizations that want to keep up with technology and remain relevant.
Developers and data scientists face tremendous challenges as they work to build the software and other digital technologies that will power our future. Going through the thoughts of various leaders, one realizes that businesses must design and create innovations that are more connected, responsive, personalized, intuitive, and productive than ever before.